diff --git a/CardiAP.ipynb b/CardiAP.ipynb
index 9ceb054..161d572 100644
--- a/CardiAP.ipynb
+++ b/CardiAP.ipynb
@@ -58,7 +58,8 @@
"\n",
"pd.set_option('display.max_rows', 20)\n",
"warnings.filterwarnings(\"ignore\", category=np.VisibleDeprecationWarning) \n",
- "warnings.filterwarnings(\"ignore\", category=RuntimeWarning) "
+ "warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
+ "uploader = {'value': {}}"
]
},
{
@@ -150,19 +151,19 @@
"source": [
"def window_open_button(url):\n",
" display(Javascript(f'window.open(\"{url.tooltip}\");'))\n",
- " \n",
+ "\n",
"def load_cv2_image_from_bytes(bytes_):\n",
" return cv2.imdecode(np.frombuffer(bytes_, dtype=np.uint8), cv2.IMREAD_COLOR)\n",
"\n",
- "def get_uploader_contents(uploader):\n",
- " return [uploader.value[file][\"content\"] for file in list(uploader.value)]\n",
+ "def get_uploader_contents():\n",
+ " return uploader['value'].values()\n",
"\n",
- "def get_uploader_filenames(uploader): \n",
- " return list(uploader.value.keys())\n",
+ "def get_uploader_filenames():\n",
+ " return list(uploader['value'].keys())\n",
"\n",
"def display_html(html):\n",
" display(widgets.HTML(value=html))\n",
- " \n",
+ "\n",
"def create_download_link( df, title = \"Download CSV file\", filename = \"data.csv\"):\n",
" csv = df.to_csv(sep='\\t')\n",
" b64 = base64.b64encode(csv.encode())\n",
@@ -170,7 +171,7 @@
" html = '{title}'\n",
" html = html.format(payload=payload,title=title,filename=filename)\n",
" return HTML(html)\n",
- " \n",
+ "\n",
"def create_inttext(description, min = None, max = None):\n",
" return widgets.BoundedIntText(\n",
" value=0,\n",
@@ -179,9 +180,9 @@
" description=description,\n",
" disabled=False\n",
" )\n",
- " \n",
+ "\n",
"def plot_analysis_result_slice(df,slice_number):\n",
- " \n",
+ "\n",
" fig, ((ax1, ax2)) = plt.subplots(2, figsize=(20,10))\n",
"\n",
" ax1.plot(df.loc[slice_number, \"amplitudes\"])\n",
@@ -192,8 +193,8 @@
" ax2.plot(df.loc[slice_number, \"intensities\"])\n",
" ax2.set_title('Intensities')\n",
" ax2.grid()\n",
- " \n",
- " plt.show() \n",
+ "\n",
+ " plt.show()\n",
"\n",
"def read_progress_img():\n",
" animatedGif = \"./assets/progress.gif\"\n",
@@ -207,9 +208,9 @@
" width=100,\n",
" height=100)\n",
"\n",
- " \n",
+ "\n",
"def render_image_visualization(analysis_results):\n",
- " render_analysis_results_visualization(analysis_results, \n",
+ " render_analysis_results_visualization(analysis_results,\n",
" title = \"Peaks\",\n",
" kind = \"image\",\n",
" download_filename = \"complete_cell\",\n",
@@ -217,48 +218,48 @@
"\n",
"\n",
"def render_slice_visualization(analysis_results, slice_number):\n",
- " render_analysis_results_visualization(analysis_results, \n",
+ " render_analysis_results_visualization(analysis_results,\n",
" title = \"Slice\",\n",
" kind = \"slices\",\n",
" download_filename = f\"slice_{slice_number}\",\n",
" progress = with_progress,\n",
- " slice_number = slice_number) \n",
- " \n",
+ " slice_number = slice_number)\n",
+ "\n",
"def render_analysis_results_visualization(analysis_results, title, kind, download_filename, progress, slice_number = 0):\n",
" \"\"\"\n",
- " Renders analysis results into three sections: \n",
+ " Renders analysis results into three sections:\n",
" - Peaks table\n",
" - Table download\n",
" - Peaks plot\n",
" \"\"\"\n",
" def filter_slice(table):\n",
" return table[table.slice_number == slice_number].drop(columns = \"slice_number\")\n",
- " \n",
+ "\n",
" display_html(f\"{title} table
\")\n",
" table, min_table = build_analysis_results_table(analysis_results, kind = kind)\n",
- " \n",
+ "\n",
" table = filter_slice(table)\n",
" display(table)\n",
- " progress(lambda: display(create_download_link(table, title = f\"Download {title} Table\", filename = f\"{download_filename}.csv\"))) \n",
- " \n",
+ " progress(lambda: display(create_download_link(table, title = f\"Download {title} Table\", filename = f\"{download_filename}.csv\")))\n",
+ "\n",
" min_table = filter_slice(min_table)\n",
" display(min_table)\n",
- " progress(lambda: display(create_download_link(min_table, title = f\"Download {title} Minimumns Table\", filename = f\"{download_filename}.csv\"))) \n",
+ " progress(lambda: display(create_download_link(min_table, title = f\"Download {title} Minimumns Table\", filename = f\"{download_filename}.csv\")))\n",
"\n",
" display_html(f\"{title} plot
\")\n",
- " progress(lambda: plot_analysis_result_slice(analysis_results[kind], slice_number)) \n",
- " \n",
- " \n",
+ " progress(lambda: plot_analysis_result_slice(analysis_results[kind], slice_number))\n",
+ "\n",
+ "\n",
"def display_results(analysis_results, shape):\n",
" \"\"\"\n",
- " Displays the results into three separate tabs. \n",
- " Every print or display will be automatically redirected to it. \n",
+ " Displays the results into three separate tabs.\n",
+ " Every print or display will be automatically redirected to it.\n",
" \"\"\"\n",
- " \n",
+ "\n",
" complete_cell_output = widgets.Output()\n",
" slices_general_output = widgets.Output()\n",
" slices_detail_output = widgets.Output()\n",
- " \n",
+ "\n",
" tab = widgets.Tab()\n",
" tab.children = [complete_cell_output, slices_general_output, slices_detail_output]\n",
" tab.set_title(0, \"Complete Cell\")\n",
@@ -270,21 +271,21 @@
" display_html(f\"Complete Cell area: {shape}
\")\n",
" render_image_visualization(analysis_results)\n",
"\n",
- " with slices_general_output: \n",
+ " with slices_general_output:\n",
" display_html(\"Slices General Data
\")\n",
" table, min_table = build_analysis_results_table(analysis_results, kind = 'slices')\n",
" display(\n",
" create_download_link(\n",
- " table, \n",
- " title = \"Download Slices Table\", \n",
- " filename = \"slices.csv\")) \n",
+ " table,\n",
+ " title = \"Download Slices Table\",\n",
+ " filename = \"slices.csv\"))\n",
" display(\n",
" create_download_link(\n",
- " min_table, \n",
- " title = \"Download Slices Minimums Table\", \n",
- " filename = \"slices_minimums.csv\")) \n",
+ " min_table,\n",
+ " title = \"Download Slices Minimums Table\",\n",
+ " filename = \"slices_minimums.csv\"))\n",
"\n",
- " with slices_detail_output: \n",
+ " with slices_detail_output:\n",
" display_html(\"Slices Detail
\")\n",
" slice_selector = create_inttext(\"Plot slice: \", min = 0, max = len(analysis_results[\"slices\"]) - 1)\n",
" plot_btn = widgets.Button(description=\"Show details\", button_style='primary')\n",
@@ -295,23 +296,28 @@
" plot_box.clear_output()\n",
" render_slice_visualization(analysis_results, slice_selector.value)\n",
"\n",
- " plot_btn.on_click(on_button_clicked) \n",
+ " plot_btn.on_click(on_button_clicked)\n",
" display(slice_selector)\n",
" display(plot_btn)\n",
" display(plot_box)\n",
"\n",
"def without_progress(code):\n",
- " code() \n",
- " \n",
+ " code()\n",
+ "\n",
"def with_progress(code):\n",
" progress = create_image(read_progress_img())\n",
" display(progress)\n",
" code()\n",
" progress.close()\n",
- " \n",
- "def clear_uploader(uploader):\n",
- " uploader._counter = 0\n",
- " uploader.value.clear()\n",
+ "\n",
+ "def clear_uploader():\n",
+ " global last_updated_number, last_file_number\n",
+ " last_updated_number = 0\n",
+ " last_file_number = 0\n",
+ " uploader['value'].clear()\n",
+ " file_chooser.reset()\n",
+ " for f in os.listdir(voila_folder):\n",
+ " os.remove(os.path.join(voila_folder, f))\n",
"\n",
"def run_discord_analysis(image):\n",
" return discord.analyze_image(image, \\\n",
@@ -333,26 +339,25 @@
"def run_cardiap_pipeline(content, shape):\n",
" \"\"\"\n",
" Loads a cell image from uploaded content bytes\n",
- " and runs it against the cardiap analyzer. \n",
- " \n",
- " This function returns the raw cardiap results dictionary and two pandas dataframes: \n",
- " \n",
- " - image: the whole cell image analysis results \n",
- " - slices: the sliced analysis results \n",
- " \n",
+ " and runs it against the cardiap analyzer.\n",
+ "\n",
+ " This function returns the raw cardiap results dictionary and two pandas dataframes:\n",
+ "\n",
+ " - image: the whole cell image analysis results\n",
+ " - slices: the sliced analysis results\n",
+ "\n",
" \"\"\"\n",
- " \n",
+ "\n",
" image = load_cv2_image_from_bytes(content)\n",
" image = image_data._apply_billateral_filter(image,ksize.value,sigma.value,sigma.value)\n",
- " \n",
" x_start = int(shape[0])\n",
" x_end = x_start + int(shape[2])\n",
- " y_start = int(shape[1]) \n",
+ " y_start = int(shape[1])\n",
" y_end = y_start + int(shape[3])\n",
- " \n",
- " image = image_data.crop_horizontal(image, x_start, x_end) \n",
- " image = image_data.crop_vertical(image, y_start, y_end) \n",
- " \n",
+ "\n",
+ " image = image_data.crop_horizontal(image, x_start, x_end)\n",
+ " image = image_data.crop_vertical(image, y_start, y_end)\n",
+ "\n",
" raw_results = run_discord_analysis(image)\n",
" raw_gradient_results = run_gradient_analysis(image)\n",
" return [{\n",
@@ -378,25 +383,25 @@
"# result = pd.concat([result,min_peaks], ignore_index=False, axis=1)\n",
"\n",
" result = result.astype({\n",
- " \"amplitudes\":\"float\", \n",
+ " \"amplitudes\":\"float\",\n",
" \"max_peaks_positions\":\"float\",\n",
" \"max_peaks_intensities\":\"float\",\n",
- " \"times_to_peaks\": \"float\", \n",
+ " \"times_to_peaks\": \"float\",\n",
" \"times_to_half_peaks\": \"float\",\n",
" \"tau_s\": \"float\",\n",
" })\n",
- " \n",
+ "\n",
" min_peaks = min_peaks.astype({\n",
" \"min_peaks_positions\":\"float\",\n",
" \"min_peaks_intensities\":\"float\"\n",
- " }) \n",
- " \n",
+ " })\n",
+ "\n",
" return result, min_peaks\n",
"\n",
"def build_analysis_results_table(analysis_results, kind):\n",
" \"\"\"\n",
" Build the final output analysis results table.\n",
- " This function works both for building slices table - where you will get a row for each slice and position - \n",
+ " This function works both for building slices table - where you will get a row for each slice and position -\n",
" or and for building image table - where you get a single row for each position\n",
" Final tables contains the same columns as raw analysis results, except by min_* columns and amplitudes\n",
" \"\"\"\n",
@@ -409,61 +414,61 @@
" @results_box.capture()\n",
" def on_button_clicked(_button):\n",
" \"\"\"\n",
- " Runs analysis and displays results. \n",
+ " Runs analysis and displays results.\n",
" It stores them in __last_analysis_results__\n",
" for debugging purposes\n",
" \"\"\"\n",
" global __last_analysis_results__\n",
" results_box.clear_output()\n",
- " \n",
- " children = []\n",
- " cells_count = uploader._counter\n",
"\n",
- " for index, (filename, content) in enumerate(zip(get_uploader_filenames(uploader), get_uploader_contents(uploader))):\n",
+ " children = []\n",
+ " cells_count = len(uploader['value'].keys())\n",
+ " print(f\"Analyzing {cells_count} cells. Please wait...\")\n",
+ " for index, (filename, content) in enumerate(zip(get_uploader_filenames(), get_uploader_contents())):\n",
" shape = crop_results[filename]\n",
- " print(f\"Analyzing cell {index + 1} of {cells_count}. Please wait...\")\n",
+ "\n",
"\n",
" output = widgets.Output()\n",
" children.append(output)\n",
"\n",
" with output:\n",
- " try: \n",
+ " try:\n",
" __last_analysis_results__ = run_cardiap_pipeline(content, shape)[0]\n",
" __last_gradient_analysis_results__ = run_cardiap_pipeline(content, shape)[1]\n",
" display_results(__last_analysis_results__, shape)\n",
- " \n",
+ "\n",
" display_html(f\"Global Discordance Results
\")\n",
" dyss_table = get_global_discordance_results(__last_gradient_analysis_results__)\n",
" display(dyss_table)\n",
" display(\n",
" create_download_link(\n",
- " dyss_table, \n",
- " title = \"Download Discordance Results\", \n",
+ " dyss_table,\n",
+ " title = \"Download Discordance Results\",\n",
" filename = \"DI_table.csv\"))\n",
- " \n",
+ "\n",
" display_html(f\"Local AR Results
\")\n",
" local_ar_table = calculate_local_AR(build_analysis_results_table(__last_gradient_analysis_results__,kind='slices')[0])\n",
" display(local_ar_table)\n",
" plot_local_AR(__last_gradient_analysis_results__).show()\n",
" display(\n",
" create_download_link(\n",
- " local_ar_table, \n",
- " title = \"Download Local AR Results\", \n",
+ " local_ar_table,\n",
+ " title = \"Download Local AR Results\",\n",
" filename = \"Local_AR_Results_table.csv\"))\n",
- " \n",
- " except (discord.PeaksError, discord.TausError) as e: \n",
+ "\n",
+ " except (discord.PeaksError, discord.TausError) as e:\n",
" display_html(f\"\"\"\n",
" \n",
" \n",
" Insufficient {e.feature()} data @ {shape}. Please try selecting a different image area or settings\n",
"
\n",
" \"\"\")\n",
- " \n",
+ "\n",
" results_box.clear_output()\n",
" results_tab = widgets.Tab()\n",
" results_tab.children = children\n",
" display(results_tab)\n",
- " for index, filename in enumerate(get_uploader_filenames(uploader)):\n",
+ " for index, filename in enumerate(get_uploader_filenames()):\n",
" results_tab.set_title(index, f\"Cell {filename}\")\n",
"\n",
"\n",
@@ -471,7 +476,7 @@
"\n",
" smoothing_title = widgets.HTML(\"Filter settings
\")\n",
" smoothing_vbox = widgets.VBox([smoothing_title, ksize, sigma], layout=box_layout)\n",
- " \n",
+ "\n",
" settings_title = widgets.HTML(\"Analysis settings
\")\n",
" settings_vbox = widgets.VBox([settings_title, slice_width, min_dist_between_maxs, calibration, analyze_btn], layout=box_layout)\n",
"\n",
@@ -486,18 +491,174 @@
"metadata": {},
"outputs": [],
"source": [
- "uploader = widgets.FileUpload(accept='.tif', multiple=True)\n",
+ "import glob\n",
+ "import tempfile\n",
+ "from ipyfilechooser import FileChooser\n",
+ "import tifffile\n",
+ "import os\n",
+ "\n",
+ "# General variables\n",
+ "global generation_process_tab, actions_vbox, file_name, file_extension, selected_file\n",
+ "selection_box = widgets.Output()\n",
+ "analysis_box = widgets.Output()\n",
+ "crop_box = widgets.Output()\n",
+ "\n",
+ "file_chooser = FileChooser()\n",
+ "\n",
+ "voila_folder = glob.glob(f\"{tempfile.gettempdir()}/{'voila'}*\")[0]\n",
+ "last_updated_number = 0\n",
+ "last_file_number = 0\n",
+ "extension_to_generate = \".jpeg\"\n",
+ "\n",
+ "# General Layouts\n",
+ "\n",
+ "box_layout = Layout(display='flex',\n",
+ " flex_flow='column',\n",
+ " align_items='stretch',\n",
+ " width='50%',\n",
+ " margin='0px 10px 10px 20px',\n",
+ " padding=' 2px 5px 0 5px',\n",
+ " justify_content='space-between')\n",
+ "\n",
+ "btn_layout = Layout(display='flex',\n",
+ " flex_flow='row',\n",
+ " align_items='stretch',\n",
+ " width='50%',\n",
+ " margin='0px 10px 5px 20px',\n",
+ " padding='2px 4% 0 1%',\n",
+ " justify_content='flex-start')\n",
+ "\n",
+ "box_upload_layout = Layout(display='flex',\n",
+ " flex_flow='flex-wrap',\n",
+ " width='50%',\n",
+ " margin='0px 10px 5px 20px',\n",
+ " padding='2px 4% 0 1%',\n",
+ " justify_contentPeaksError='space-between')\n",
+ "\n",
+ "# Selection File Section\n",
+ "\n",
+ "html_text_upload = \" Select your file to initialize the analysis
\"\n",
+ "header_text_upload = widgets.HTML(value=html_text_upload)\n",
+ "upload_vbox = widgets.VBox([header_text_upload, file_chooser], layout=box_upload_layout)\n",
+ "\n",
+ "# Pre-crop Control Section\n",
+ "\n",
+ "start_btn = widgets.Button(description=\"Start\", button_style='primary', icon = 'fa-play')\n",
+ "clear_btn = widgets.Button(description=\"Clear\", button_style='danger', icon = 'fa-trash', button_color = 'salmon')\n",
+ "\n",
+ "def display_the_pre_crop_controls_section():\n",
+ " global actions_vbox\n",
+ " actions_vbox = widgets.VBox([start_btn, clear_btn], layout=btn_layout)\n",
+ " display(actions_vbox)\n",
+ "\n",
+ "# Image Generation Confirmation Section\n",
+ "\n",
+ "generation_process_box_layout = box_layout\n",
+ "generation_process_box_layout.width = '450px'\n",
+ "generation_process_btn_layout = Layout(display='flex', flex_flow='row', margin= 'auto')\n",
+ "generation_process_content_question = widgets.Label(value=\"Are you sure you want to continue with the process?\")\n",
+ "generation_process_content_recommendation = widgets.Label(value=\"It is recommended to have at least twice the size of the original file.\")\n",
+ "generation_process_content_top_section = widgets.VBox([generation_process_content_question, generation_process_content_recommendation])\n",
+ "generation_process_confirm_btn = widgets.Button(description=\"Confirm\", button_style='primary', icon = 'fa-play', button_color= 'forestgreen')\n",
+ "generation_process_cancel_btn = widgets.Button(description=\"Cancel\", button_style='danger', icon = 'fa-ban', button_color= 'salmon')\n",
+ "generation_process_content_bottom_section = widgets.VBox([generation_process_confirm_btn, generation_process_cancel_btn], layout=generation_process_btn_layout)\n",
+ "\n",
+ "@selection_box.capture()\n",
+ "def display_the_generation_process_section(_change):\n",
+ " global generation_process_tab, file_name, file_extension, selected_file\n",
+ " selected_file = file_chooser.selected\n",
+ " file_name, file_extension = os.path.splitext(file_chooser.selected_filename)\n",
+ " generation_process_content = [widgets.VBox([generation_process_content_top_section, generation_process_content_bottom_section])]\n",
+ " generation_process_tab = widgets.Tab(children=generation_process_content, layout=generation_process_box_layout)\n",
+ " generation_process_tab.set_title(0, \"Generation Process\")\n",
+ " if not is_image(file_extension):\n",
+ " display(generation_process_tab)\n",
+ " else:\n",
+ " selecting_and_generating_temp_images(_)\n",
+ "\n",
+ "file_chooser.register_callback(callback=display_the_generation_process_section)\n",
+ "\n",
+ "def clear_the_generation_process_section(_button):\n",
+ " generation_process_tab.close()\n",
+ " actions_vbox.close()\n",
+ " clear_uploader()\n",
+ "\n",
+ "generation_process_cancel_btn.on_click(clear_the_generation_process_section)\n",
+ "\n",
+ "def is_image(file_extension):\n",
+ " image_extension = [\".jpeg\",\".jpg\",\".png\",\".svg\",\".tif\"]\n",
+ " return file_extension in image_extension\n",
+ "\n",
+ "def is_video(file_extension):\n",
+ " video_extension = [\".mp4\",\".avi\",\".flv\",\".3gpp\",\".mkv\",\".mks\", \".mov\"]\n",
+ " return file_extension in video_extension\n",
+ "\n",
+ "def is_slm(file_extension):\n",
+ " return file_extension == \".lsm\"\n",
+ "\n",
+ "@selection_box.capture()\n",
+ "def selecting_and_generating_temp_images(_button):\n",
+ " it_was_valid = True\n",
+ "\n",
+ " if is_image(file_extension):\n",
+ " image_extractor(selected_file, file_name)\n",
+ " elif is_video(file_extension):\n",
+ " video_frame_extractor(selected_file, file_name)\n",
+ " elif is_slm(file_extension):\n",
+ " lsm_frame_extractor(selected_file, file_name)\n",
+ " else:\n",
+ " it_was_valid = False\n",
+ " clear_the_generation_process_section(_)\n",
+ " print(f\"Error: The selected file does not have a valid extension {file_extension}\")\n",
+ "\n",
+ " if it_was_valid:\n",
+ " generation_process_tab.close()\n",
+ " display_the_pre_crop_controls_section()\n",
+ "\n",
+ "generation_process_confirm_btn.on_click(selecting_and_generating_temp_images)\n",
+ "\n",
+ "def save_temp_file(frame, selected_file_name, temp_folder):\n",
+ " global last_file_number\n",
+ " full_file_name = f\"{selected_file_name}_{last_file_number}.jpeg\"\n",
+ " generated_path = os.path.join(temp_folder, full_file_name)\n",
+ " rows, columns, = frame.shape\n",
+ " max_size = max(rows, columns)\n",
+ " square_matrix = np.zeros((max_size, max_size), dtype=np.uint8)\n",
+ " square_matrix[:rows, :columns] = frame\n",
+ " cv2.imwrite(generated_path, square_matrix)\n",
+ " last_file_number += 1\n",
+ "\n",
+ "def image_extractor(image_file, selected_file_name):\n",
+ " save_temp_file(cv2.cvtColor(cv2.imread(image_file), cv2.COLOR_BGR2GRAY), selected_file_name, voila_folder)\n",
+ "\n",
+ "def video_frame_extractor(video_file, selected_file_name):\n",
+ " capture = cv2.VideoCapture(video_file)\n",
+ " success, frame = capture.read()\n",
+ "\n",
+ " while success:\n",
+ " grayscale_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
+ " save_temp_file(grayscale_frame, selected_file_name, voila_folder)\n",
+ " success, frame = capture.read()\n",
+ "\n",
+ " capture.release()\n",
+ "\n",
+ "def lsm_frame_extractor(lsm_file, selected_file_name):\n",
+ " lsm_data = tifffile.imread(lsm_file)\n",
+ "\n",
+ " for _, frame in enumerate(lsm_data):\n",
+ " frame_normalized = frame / np.max(frame)\n",
+ " frame_uint8 = (frame_normalized * 255).astype(np.uint8)\n",
+ " save_temp_file(frame_uint8, selected_file_name, voila_folder)\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
"\n",
- "def on_uploader_changed(_uploader):\n",
- " \"\"\"\n",
- " Override uploader._counter to \n",
- " always be in sync with actual uploaded values. \n",
- " \n",
- " This is most probably a widgets bug\n",
- " \"\"\"\n",
- " uploader._counter = len(uploader.value)\n",
- " \n",
- "uploader.observe(on_uploader_changed, 'value')\n",
"\n",
"ksize = widgets.IntText(\n",
" value=0,\n",
@@ -529,17 +690,7 @@
" disabled=False\n",
")\n",
"\n",
- "html_text_upload = \" Upload your image to initialize the analysis
\"\n",
- "header_text_upload = widgets.HTML(value=html_text_upload)\n",
- "\n",
- "box_upload = Layout(display='flex',\n",
- " flex_flow='flex-wrap',\n",
- " width='50%',\n",
- " margin='0px 10px 5px 20px',\n",
- " padding='2px 4% 0 1%',\n",
- " justify_contentPeaksError='space-between')\n",
"\n",
- "upload_vbox = widgets.VBox([header_text_upload, uploader], layout=box_upload)\n",
"\n",
"box_layout = Layout(display='flex',\n",
" flex_flow='column',\n",
@@ -549,24 +700,6 @@
" padding=' 2px 5px 0 5px',\n",
" justify_content='space-between')\n",
"\n",
- "btn_layout = Layout(display='flex',\n",
- " flex_flow='row',\n",
- " align_items='stretch',\n",
- " width='50%',\n",
- " margin='0px 10px 5px 20px',\n",
- " padding='2px 4% 0 1%',\n",
- " justify_content='flex-start')\n",
- "\n",
- "\n",
- "start_btn = widgets.Button(description=\"Start\", button_style='primary', icon = 'fa-play')\n",
- "clear_btn = widgets.Button(description=\"Clear\", button_style='danger', icon = 'fa-trash')\n",
- "\n",
- "clear_btn.style.button_color = 'salmon'\n",
- "actions_vbox = widgets.VBox([clear_btn, start_btn], layout=btn_layout)\n",
- "\n",
- "crop_box = widgets.Output()\n",
- "analysis_box = widgets.Output()\n",
- "\n",
"@crop_box.capture()\n",
"def on_start_button_clicked(_button):\n",
" \"\"\"\n",
@@ -576,28 +709,68 @@
" \"\"\"\n",
" global __last_crop_results__\n",
" __last_crop_results__ = {}\n",
- " contents = get_uploader_contents(uploader)\n",
+ " contents = get_uploader_contents()\n",
+ " names = uploader['value'].keys()\n",
" clear_job_boxes()\n",
- " \n",
+ "\n",
" @analysis_box.capture()\n",
" def callback(image_name, shape):\n",
- " __last_crop_results__.update({image_name: shape.size})\n",
" if len(contents) == len(__last_crop_results__):\n",
" analysis_box.clear_output()\n",
" render_results_box(__last_crop_results__)\n",
- " \n",
- " if uploader._counter == 0:\n",
- " print(\"Please upload an image first\")\n",
+ "\n",
+ " if last_file_number == 0:\n",
+ " print(\"Error: To start the analysis phase, select a file first\")\n",
+ " elif is_video(file_extension) or is_slm(file_extension):\n",
+ " def upload_images_to_memory(base_file_name, temp_folder, format_file):\n",
+ " global last_updated_number\n",
+ " limit = last_updated_number + 10\n",
+ " generated_names = tuple()\n",
+ " while (last_file_number != last_updated_number) and (limit != last_updated_number) :\n",
+ " full_file_name = f\"{base_file_name}_{last_updated_number}\"\n",
+ " generated_path = os.path.join(temp_folder, full_file_name + format_file)\n",
+ " with open(generated_path, 'rb') as image_file:\n",
+ " uploader['value'][full_file_name] = image_file.read()\n",
+ " generated_names = generated_names + (full_file_name,)\n",
+ " last_updated_number += 1\n",
+ "\n",
+ " return generated_names\n",
+ "\n",
+ " def change_selection(change):\n",
+ " selected_value = change['new']\n",
+ " image_preview.value = uploader['value'][selected_value]\n",
+ "\n",
+ " def more_items_dropdown(_button):\n",
+ " new_values = upload_images_to_memory(file_name, voila_folder, extension_to_generate)\n",
+ " dropdown_images.options = dropdown_images.options + new_values\n",
+ " dropdown_images.value = new_values[0]\n",
+ " image_preview.value = uploader['value'][dropdown_images.value]\n",
+ "\n",
+ " image_preview = widgets.Image()\n",
+ " dropdown_images = widgets.Dropdown()\n",
+ " more_items_dropdown(_)\n",
+ " select_btn = widgets.Button(description=\"Select\", button_style='primary', icon = 'fa-check')\n",
+ " more_image_btn = widgets.Button(description=\"More Images\", button_style='primary', icon = 'fa-plus', style={'button_color': 'forestgreen'})\n",
+ " dropdown_vbox = widgets.VBox([dropdown_images, image_preview], layout=box_upload_layout)\n",
+ " dropdown_buttons = widgets.VBox([select_btn, more_image_btn], layout=btn_layout)\n",
+ " display(dropdown_vbox)\n",
+ " display(dropdown_buttons)\n",
+ "\n",
+ " more_image_btn.on_click(more_items_dropdown)\n",
+ " dropdown_images.observe(change_selection, names='value')\n",
+ "\n",
" else:\n",
- " images_buffers = [np.array(Image.open(io.BytesIO(buffer))) for buffer in contents] \n",
- " display(crop(images_buffers, image_name_list = get_uploader_filenames(uploader), continuous_update = False, optimize = False, callback = callback))\n",
- " \n",
- "start_btn.on_click(on_start_button_clicked)\n",
- " \n",
+ " full_file_name = f\"{file_name}\"\n",
+ " generated_path = os.path.join(voila_folder, full_file_name + extension_to_generate)\n",
+ " with open(generated_path, 'rb') as image_file:\n",
+ " uploader['value'][full_file_name] = image_file.read()\n",
+ "\n",
"def on_clear_button_clicked(_button):\n",
- " clear_uploader(uploader)\n",
+ " actions_vbox.close()\n",
+ " clear_uploader()\n",
" clear_job_boxes()\n",
"\n",
+ "start_btn.on_click(on_start_button_clicked)\n",
"clear_btn.on_click(on_clear_button_clicked)"
]
},
@@ -722,8 +895,7 @@
" athors_section_title, \n",
" athors_section_description, \n",
" citations_section_title, \n",
- " citations_section_description, Runing_section], layout=layouts_intro)\n",
- "\n"
+ " citations_section_description, Runing_section], layout=layouts_intro)"
]
},
{
@@ -822,7 +994,7 @@
"display(nav_bar_vbox)\n",
"display(main_section_vbox)\n",
"display(upload_vbox)\n",
- "display(actions_vbox)\n",
+ "display(selection_box)\n",
"display(crop_box)\n",
"display(analysis_box)"
]
diff --git a/requirements.txt b/requirements.txt
index f4a41bd..998b52e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,3 +8,5 @@ ipython==7.21.0
seaborn==0.12.2
matplotlib==3.6.3
interactivecrop==0.0.10
+tifffile==2023.7.10
+ipyfilechooser==0.6.0
\ No newline at end of file